The Productivity Cycle: Transforming Your Workforce from an Expense to a Profit Center

Note: Images from the original text are omitted in this text version.

Executive Summary

Company profits are driven, directly or indirectly, by the performance of rnevery employee. Performance data for specific positions, carefully selected from rnavailable metrics, can be used to improve each employee. Productive employees rnwill in turn increase the output of a position as a whole, which will lead to rnincreased company profits. But job effectiveness can only be maximized through rnthe use of proper performance metrics that accurately define success in a rnspecific position at the individual level.

This white paper provides specific steps to help you identify your strongest rnemployee performance data, then transform that data into a repeatable process rnthat will increase position productivity to its fullest potential through rnhiring, training, and employee development. Before you know it, your workforce rnbecomes the engine that drives profits to new levels.

Converting Performance Data to Profit Dollars

Can I share a deep, dark secret? I am terrible when it comes to color rncoordination. You would not believe the number of times I am told, “That outfit rndoesn’t match.” Every time I hear criticism, I find myself thinking, “What are rnthey talking about? It looks great to me!” On the bright side, someone very rnsmart invented the color wheel for people like me. The beauty of the color wheel rnlies in its simplicity. This well-designed model not only represents the primary rncolors, but it also illustrates how they are interrelated and which colors rncomplement one another.

In contrast to the color wheel, many times in business we overcomplicate our rnworkforce models by using crazy strategies, dotted-line structures, complicated rncompetencies, or other popular attempts to improve productivity in the rnworkplace. Sometimes complicated solutions are the best answer. In contrast to rnthose complicated models, the Productivity Cycle (shown at left) provides rnspecific steps to help you catalog employee performance data, then transform rnthat information into a system that increases position productivity and drives rnprofits for the organization.

You will find that the Productivity Cycle provides a simple visual rnrepresentation of the steps needed to align people and profit. Like the color rnwheel, the center of the cycle contains the primary stages: Catalog, Transform, and Systemize. Each stage is supported by secondary actions that guide the user rnaround the wheel. These steps are represented by different shades of color rnwithin each primary stage. You progress clockwise around the Productivity Cycle rnas you move your workforce into a profit center.

Catalog

By cataloging your available performance metrics, you embark on the path of rnmaximizing the performance of your people. But you must know where you are rnbefore you can determine where you need to go. This principle applies to your rnperformance data. The first stage, the green area in the model, is designed to rnhelp identify and understand performance data as it relates to an individual in rna specific position.

Learning Objectives
• Learn to classify the different types of metrics that are important to rnemployee performance.
• Learn how to collect the right performance data in the proper manner to rnincrease the accuracy of your findings.
• Learn to formulate the tough questions that help you choose which data best rnpromotes profitability through people.

Classify

The easiest way to understand performance data is to view it on a continuum.

Soft Metric: What Is It?

Soft metrics, on the left end of the continuum, describe any evaluation method rnthat relies heavily on a person’s judgment. Soft metrics can take many forms, one of the most basic being when a supervisor ranks employees from the “best rnperformer” to the “worst performer” based on the supervisor’s opinion. Another rnexample may take the form of a subjective label. This scenario would entail a rnsubjective ranking of each employee (Good, Better, Best, or A, B, C, etc.). Typically, there is not much science wrapped around this process. Practically, a rnsupervisor would sit down, think back to their perception of individual rnperformance, and apply a subjective label based on opinion and very little, if rnany, objective criteria. When I see this evaluation method, I like to call it rnthe “I know my people” approach.

To align your employees with profitability, you should only use soft metrics as rna short-term solution and a first step toward more accurate performance rnmeasures. Soft metrics can be used in placed of real data in situations where rnthere is no data available, but in the long-term you should be moving to systems rnor programs that replace subjectivity with objective performance evaluation. Soft metrics should not be used in place of performance data that is tied rndirectly to actual performance on the job. I have observed many corporate rnexecutives who felt that they had a very tight grasp (without actual data) on rnwho their best and worst performers were. Each and every time we compared the rnexecutive perception against actual performance, there was a sizable disconnect rnbetween perception and reality based on the data. The point is to move your rnorganization away from taking the “I know my people” approach as quickly as you rncan.

Performance Appraisal: What Is It?

In the middle of the continuum, we find one of the most popular forms of rnevaluation: the performance appraisal. This shift away from pure soft metrics rnrepresents a reliance on subjective opinions, but those opinions are documented rnusing a standardized evaluation. Let me explain further. This method of rnevaluation involves a person who possesses firsthand knowledge of each rnemployee’s daily performance. However, the performance appraisal differentiates rnpeople through the use of standardized formats that capture performance rnperceptions.

For example, a supervisor is supplied with a form that captures job components rnor critical aspects of the position that have been studied and proven vital to rnsuccess in the role. These job components may include items such as Work Ethic (reliable attendance, diligence in follow-up activities, positive attitude), Communication Skills (conveys ideas clearly, resolves conflict), or Project Management (meets deadlines, organized). The supervisor will actually rate rnemployees one at a time on each critical aspect of the job. Sample performance rnratings might be “Ineffective” to “Highly Effective,” or use a numeric scale of 1 to 5, or cover a range from “Does not meet expectations” to “Exceeds rnexpectations,” or thousands of other variations. This approach documents the rnareas where employees are doing well, as well as where they may need rnimprovement, through a standardized system that translates general perception rninto specific ratings regarding actual aspects of the job.

A performance appraisal tool can be an effective way to capture the opinions of rnmanagement in relation to employee performance. Appraisals are a popular form of rnperformance evaluation because in many positions it is difficult to quantify rnperformance at the individual level. In fact, we studied a sample of 37,055 rnpeople in 487 various positions in different companies and found that 69% of rnthese positions relied on performance evaluation tools as their primary form of rnmeasurement. In addition, performance appraisal tools provide a flexible method rnof quantifying performance based on the opinions of those who observe the rnemployees at work—primarily their managers.

Be aware of potential sticky issues associated with performance appraisals. Obviously, one such issue is the subjective nature of the evaluation. This rnemphasis on opinion often introduces inconsistencies across different rnorganizational groupings, such as geographies, departments, and locations. For rnexample, a manager in one area of the country may tend to rate incumbents much rnlower than managers in other areas. This may make evaluating employee rnperformance across different groups difficult. A similar problem may be found rnwhen the performance appraisal contradicts other performance metrics. This lack rnof alignment often points to inconsistencies between managerial opinion and rnnumerical performance. There may be a number of reasons for the lack of rnalignment, but there is always a high potential for inconsistency when human rnopinion is at the center of the appraisal process.

Even though a performance evaluation is a popular tool, many companies are led rnastray by the simplicity and ease of deployment throughout the company. If you rnare truly pursuing an alignment of your employees to profit, you should do rneverything in your power to go straight to the source—the numbers. Many rncompanies do a very good job of creating performance appraisal systems. The data rncollected from these systems are high quality and as sound as can be. But when rnthe performance appraisal results for individual employees are compared to the rnactual output numbers (in cases where the ratings are not based on the numbers), there may be no relationship, and often presents a negative relationship. Be rnsure that you do not rely solely on the ratings. Challenge yourself to find ways rnto evaluate jobs with actual data.

Hard Metric: What Is It?

The right end of the continuum represents hard metrics. A hard metric is best rndescribed as objective data that directly represents quantifiable information. These types of metrics are typically linked directly to an organization’s bottom rnline. Some examples of these metrics include throughput numbers, calls answered, percentage of quota, quality scores, number of units sold, total sales, average rnhandle time, or any measure directly related to job performance. Hard metrics rnprovide valuable insights into the numerical productivity of a person in rnvirtually any position. From a company’s perspective, the appeal of hard metrics rnstems from the objectivity of the data. Hard metrics are not adjusted or rninfluenced by human opinion. As long as the role stays the same and the data is rncollected in the same way, hard metrics are a dependable measure of performance.

You will come across some jobs that do not appear to possess clear, hard rnmetrics. In this situation I would encourage you to remember the phrase “work = rnoutput.” What we get paid for is called work because there is an expected rnoutput. It is simply a matter of collecting information surrounding the skills, abilities, responsibilities, tasks, and expectations of the job. Then use that rninformation to create ways to quantify the output of the position and rnsystematically collect performance data. With a little time, effort, and rncreativity you will find that nearly any position can be numerically classified rnin terms of hard metrics.

Collect

Now that you know how to classify performance data, the first step is to collect rnthe data. Later we will be able to evaluate its usefulness. Have you ever heard rnthe saying, “The devil is in the details”? Likewise, your ability to transform rnyour workforce from an expense to a profit center can be derailed quickly during rnthe action step of data collection. Prior to collecting the data, you will need rna few safeguards to ensure the consistency, accuracy, and accessibility of the rndata collection process will not affect the interpretability of the metric.

Consistency of the data collection process is very important. Everyone involved rnin data collection should understand and adhere to the specifics of the data rncollection process. Inconsistent data collection methods will lead to inaccurate rncomparisons among individual performers. Pay special attention to location or rnregional differences. Inaccurate evaluations of performance will contaminate any rnfuture findings and reduce the effectiveness of your future adjustments. Think rnof consistency in terms of a simple illustration. If I ask all my district rnmanagers to give me their turnover numbers, I may receive percentages from each rndistrict manager but the numbers may mean many different things. Some may have rngiven me annual turnover, some turnover for a single month, and others may have rngiven me involuntary turnover only. The point is to be careful and ensure your rndata collection processes drive consistency.

Accuracy of the performance data being collected is also an important phase of rnthe collection process. Accuracy must be a priority when interpreting individual rnperformance. Later in this process, inaccurate data will lead to false rnconclusions and bad decisions when evaluating and developing your employees. Think of inaccurate data as the enemy of transforming your workforce. After you rnhave collected your data, use these “red flags” to alert you to potential rninaccuracies in the data:

• Incomplete data or cases where it is commonplace to find no information.
• The use of “0.” Is that “0” representing actual performance or a blank entry?
• Data presented in a number of different formats – for example, half of the rndata is presented in percentages and half as round numbers.
• Odd outliers – for example, most of the cases in a data set contain rnsingle-digit performance measures, but some cases show triple digit measures.
• Labels do not match the data – for example, “Dollars Sold” is the label, but rnthe data is presented in percentages.
• Conflicts in columns – for example, an employee with a September hire date has rnperformance data recorded from March of the same year.

Another factor to consider is the accessibility of the performance data. Sophisticated human resource information systems (HRIS), payroll systems, and rnperformance management systems are helpful tools as long as you have easy access rnto the data. Avoid situations where the data is difficult to collect and study. All too often companies focus on collecting performance data at the aggregate rnlevel and neglect to collect and study it at the individual level. Whether the rndata is performance ratings, quality scores, or sales figures, make sure your rndata collection systems are tied to individual performance.

Another valuable tip to consider when collecting a performance metric is the rnnumber of data points, or employee observations, represented in the data set. Whenever possible, it is beneficial to have access to multiple observations of rnthe performance data. For example, monthly observations would be richer than a rnsimple yearly total or average for the year. Anytime the data is aggregated, there is a chance that you will lose some valuable information that may be rnhelpful in understanding performance trends related to the position. When rncollecting your data, always focus on your objective, which is to obtain the rnbest data that will lead to the richest amount of information.

Now it is time to collect data. Apply the principles you have learned about rnperformance data to collect the cleanest data set that you can. It is a good rnpractice to initially overshoot the amount of data you would reasonably expect rnto use. Collect many types of metrics and forms of performance data for each rnposition. This practice gives you multiple measures of performance, but more rnimportantly, it helps you choose the best combination of performance indicators rnby providing options (different performance data) as we will discuss later.

Choose

After the performance data has been collected, there are several choices you rnneed to make to help identify the best metric(s) to focus on. In order to make rnthe best choices, there are a few things to consider. Specifically, does the rndata you captured reflect variability, job-relatedness, and a relationship to rnyour business objectives (keep reading for an explanation of these terms)? Throughout this process, it is important to understand that as soon as your rnperformance metric is specified, it will begin to shape and guide the direction rnof your workforce. All future performance, evaluation, and developmental rnactivities in that position will be directly influenced by the metric. Therefore, choosing the right metrics to follow is an important consideration to rndrive the future of your business.

Variability is ensuring that the data metric represents all performance levels. Ask yourself this question: Does the metric differentiate between individuals’ rnperformance levels? Oftentimes, performance metrics are consistently collected rnand accurate, but they lack variability in performance scores. I once worked rnwith a company that insisted a particular quality rating was its main indicator rnof performance for its call center representatives. Upon further review of the rndata, we found that the average score was nearly 100%, with only a handful of rnincumbents receiving a lower score of 98-99%. This data offers no useful rnmeasurement because it implies that each employee is performing at the same high rnlevel, with no variances to highlight specific performance conce s. Any rnbusiness leader would have a hard time choosing a metric with no variability; therefore, this type of data offers little, if any, real value.

Job relatedness is another important issue to consider when choosing the best rndata on which to shape your future workforce. Determine how much influence an rnindividual has on the performance metric. In all cases, direct influence is rnbest. The less influence incumbents have on the metric, the less descriptive it rnis of their actual performance. In an ideal situation, you will have great rnconfidence that your performance data is related to the job and that each rnincumbent is able to affect that metric directly.

For example, a car dealership can track the number of cars that are sold by its rnsalespersons, as well as how many of those sold cars are returned to the rndealership’s service department for repairs. When looking for job-related sales rnmetrics, the former measure is good, but the latter is unrelated to sales. Does rna salesperson have an influence on the mechanical soundness of the car he sells? No, he only has control of the selling process. Relying on a metric with little rnrelation to actual job activities will lead to inaccurate conclusions. Additionally, your mindset should be in pursuit of truth as it relates to real rnperformance on a daily basis. This truth can only be found if the data is rnrelated to performance in the position.

Business drivers—it is time to think strategically! Think in terms of the rndirection that you want to take your business, and then the position-specific rnmetrics will move each position in that direction. Alignment can be found by rnworking backwards. Ask yourself how each position fits into your business rnstrategy or contributes to the financial performance. Then determine the rnindividual performance metrics that best align to the position and allow you to rntrack your progress toward achieving your business goals. Referring again to our rncar salesperson example, a strong business driver might be “number of cars rnsold.” If it does not drive bottom-line profit, it should not be a cornerstone rnof your performance data.

Evaluating your individual performance data in terms of variability, being rnjob-related, and being a business driver is a major strategic step in the rnprocess of transforming your workforce from an expense to a profit center, thereby directly improving the productivity of your people in driving your rnbusiness.

Summary: Finding Ideal Performance Data for a Position

Now that we have explored the Catalog stage, you have learned how to:
• Classify performance data according to what is available, useful, and rnfeasible.
• Collect the data from individual performers in a specific position.
• Choose the performance data that reflects variability, job-relatedness, and a rnrelationship to your business objectives.

At this point, you should have performance data selected and collected for each rntargeted position so that you can turn that knowledge into the building blocks rnfor a position-specific template.

Transform

As previously stated, the goal of this white paper is to help you identify your rnstrongest employee performance data, then transform that data into a repeatable rnprocess that will maximize productivity. In the last section we classified, collected, and selected the strongest measures of employee performance. Now we rnexamine the Transform phase of the process in which your performance data is rnmatched to the actual job behaviors strongly related to success in the position. By determining which traits are most important to good performance, we can then rnbuild a position template that organizes those traits, and then translate that rnposition template into desired behaviors specific to the job. The Transform rnphase brings you closer to the ideal workforce that drives profitability for the rnorganization.

Learning Objectives

• Learn to recognize key traits that tell you how a person is successful in a rnposition.
• Learn tips on how to create a job level position template that targets the rntraits necessary for success.
• Learn to translate the traits within a position template into job-related rnbehaviors that reflects those who are producing more, and contrast their rnbehaviors with less productive individuals.

Traits

At this point in the Productivity Cycle, we have focused on the critical aspect rnof cataloging performance data. Although the performance data indicates the rnresult of each person’s efforts, it does not tell you how they achieved their rnresults, nor will it tell you how inte al or exte al candidates for the rnposition will perform on the job. Therefore, we need to spend time discussing rnthe first component of the Transform phase—identification of traits.

Behaviors, or traits, that drive performance are best determined by “letting the rndata speak” as opposed to making “educated guesses.” A time-tested method of rnidentifying traits, skills, and other relevant pieces of job-related information rncomes from the use of a job analysis. A job analysis collects clues as to what rnis needed to properly execute a job.

There are many methods to analyze a job. One common method is to send out a job rnquestionnaire to experts in the role, asking them to document their opinion on rnthe important tasks or traits needed to be successful. Another method is to rnmanually observe and document the traits needed for success. However you package rnit, the basic idea is to manually study and document aspects of the job. A job rnanalysis provides solid information about the minimum qualifications and skills rnnecessary for a role. But a typical job analysis will fall short when you want rnto gather a deeper insight into the actual performers in a position. Said rnanother way, a job analysis will not provide you a vehicle to “get in the heads” of those who are successful and compare them to those who are not successful in rna role.

It is important not to confuse minimum qualifications with actual predictive rnperformance. Many people make the mistake of assuming that meeting the minimum rnqualifications is the finish line. For example, a job analysis may indicate that rnit is necessary for incumbents to operate a particular phone system. After the rnsecond day of training, everyone understands the phone system and can rneffectively operate it. Even though using the phone is essential for daily rnperformance, it exhibits no relationship with real success on the job. Being rnable to perform a job and being successful at it are two very different rnconcepts. Your goal for each position in your company should be strong rnperformance, not simply getting by.

To achieve the goal of strong performance, you must dig deeper into the actual rntraits and behaviors that drive success. Behavioral assessments are a very rneffective way to collect data on the traits of individuals from all performance rnlevels in the position. A behavioral assessment is a tool or, as I call it, a rnvehicle for data collection that extracts information from individuals related rnto their behavioral preferences. These traits, in addition to performance data, will provide the data needed to help identify how employees have success or rnfailure in a position. Specifically, behavioral assessments will provide you rnwith insight into individuals’ preferences related to how they approach rnproblems, process information, interact with others, and respond to various work rnsituations. Typically, this information is collected through a series of rnquestions presented to the individual using a questionnaire. The answers are rntu ed into conclusions that represent specific preferences or behavioral traits rnthat provide clues into how and why people do what they do when working.

Template

Be careful—it is not all about the performance data in the job analysis or the rnresults of the behavioral assessment. It is about how you use the two together rnto transform performance data into a template of targeted behavioral traits. To rnfully capture the traits most conducive to success in a position, you need to rnlet your business drivers (performance data) dictate the importance and amount rnof each trait. The assumption that more of each trait is best will lead you down rnthe wrong path. Consider a trait such as “independence” in an individual rncontributor role. A successful person in this role is measured in terms of rnthroughput. This position requires an employee to sit at a desk and complete rnrepetitive tasks in accordance with specific instructions from a manager. Think rnabout it—would someone who is extremely independent-minded be successful in this rnrole? In this case, it is safe to assume that an individual’s desire for rnindependence would actually inhibit their performance.

Using Technology to Measure Traits

When developing a position template (Performance Data + Traits), you should rnbegin by identifying the traits of successful people that differentiate them rnfrom their less successful co-workers. Technology is often used to simplify this rnprocess. Most behavioral assessment tools generate numerical representations of rnan individual’s behavioral traits. These numerical representations are often rncalled dimension scores, characteristic scores, factor scores, or many other rnassessment-specific names. The basic idea is to provide you with information rnthat plots a person’s trait on a scale where you can better understand how that rnperson compares to others for each characteristic. Most behavioral assessment rntools offer many traits used to describe the individual. Either way, technology rnwill enable you to quickly and accurately collect trait information. Additionally, utilizing assessment technology will streamline your ability to rnmake statistical comparisons between individual performers. The final objective rnis to use performance data to discover the traits that are most predictive of rnsuccess in the position.

The specific steps listed below will help you create a position template with rnthe use of technology.

• Statistically search for the relationships between traits and performance rndata.
• Within a position, split your employees into groups based on their performance rndata.
• Calculate trait score descriptive statistics (average, median, standard rndeviation, etc.) for each performance group.
• Compare performance groups by descriptive statistics.
• Search for any hidden patterns of traits among performance groups.

If Technology is Not an Option

If assessment technology is not available in your situation, let me suggest a rnfew pointers that may guide you in your efforts to creating a position template. First, ask your subject matter experts if they have any theories as to which rntraits enable individuals to be successful in the role. Then, compare the traits rnbased on the experts’ theories to the performance data you have collected. The rngoal is to determine if the theories are supported or contradicted by the data. Think of this as a process of taking something from theory to reality. The key rnis not to take the experts at their word, but to apply the theory against actual rnperformance data and attempt to confirm or deny the theory. A good illustration rnof this concept comes from the retail sector. A certain group of executives rntheorized that successful store managers were very ambitious. However, as we rncollected information at the individual level, we found that successful managers rnhad been in their role for many years and were very comfortable with their rncontribution to the company. There was no desire to move up or out, so the rnassumption of “high ambition” did not prove to be accurate.

Here are a few specific steps that may help you in creating a position template rnwithout the use of technology.

• Use the performance data to create subgroups based on performance level.
• Count the percentage of people in each subgroup who possess unique traits.
• Document the commonalities among the performance groups.
• Compare and contrast the characteristics across performance groups.
• Find those characteristics that stand out and differentiate performance rnlevels.
• Use your findings to create new areas to study—keep digging.

Group Traits

It is important to remember that, at this point in the cycle, you are looking rnfor group traits, not the traits of one individual performer. Focusing on only rnone individual as the ideal employee for a position will eventually lead you to rninaccurate conclusions. This is true because some trait studies contain rnanomalies, such as successful individuals whose approach to work is unique when rncompared to the other successful people. Understanding the “group” concept will rnhelp you ensure that your position template is based on traits that can be rnreplicated by others. The template, once created for each position, becomes a rnpowerful tool that can be used to directly align individuals with real rnperformance objectives.

Translate

You should have now identified the traits required in the position (both key rnbehaviors and optimum targets based on performance data), and built a position rntemplate of those traits. The next step is to make the position template useful rnin practice. It is important to note that this step is not changing the rntemplate, but simply understanding what the position template means for the rnorganization.

So right now you may be thinking, “This is a great exercise, but how can a rnposition template impact daily performance?” This is the exciting part! Because rnyour template is based on desired performance (performance data), it represents rnthe individual traits that have exhibited relationships to individuals rnperforming in a desirable manner. However, we want to make sure the position rntemplate can be used on a daily basis. By translating the traits of the template rninto job-related behaviors, you will better understand those who are producing rnmore, and contrast their behaviors with less productive individuals. This rnenables you to apply the information in a way that drives your workforce toward rnactual productivity results while aligning closely to your business drivers.

For example, imagine that you are analyzing the cashier position in a grocery rnstore. While collecting job traits for your position template, you discover that rncashiers need some level of sociable behavior to perform successfully. “Sociability” becomes a part of the position template that you are building. Your observations indicate that the best performers seem to be moderately social rnwhile lower-performing cashiers tend to be extremely social. These findings may rncontrast with logic (the more friendly the cashier, the better), but the rnperformance data supports the moderately sociable trait.

You can now translate that trait into actual practice. According to your earlier rnjob analysis, it is the cashier’s job to be friendly while maintaining a focus rnon productivity. Overly social cashiers attempt to have deep and meaningful rnconversations with every shopper, causing long lines and dissatisfied customers, while the social moderates can engage in small talk with customers while keeping rntheir lines moving. By translating the sociability trait, we establish the link rnbetween the trait and performance on the job of those who are producing more.

Summary: Transforming performance data into traits that drive work-related rnbehavior.

By following the transformation process from trait collection, to position rntemplate creation, through translation of the position template into rnwork-related behaviors, your position template becomes a practical tool to rnfine-tune your workforce. The position template gives you a scientific method to rnanalyze the traits that differentiate performance for any position. Additionally, the template gives you the direct link needed to move your rnworkforce from an expense to a profit center.
Systemize

After all of the hard work put into creating your position template, you want to rnbe sure that it is fully utilized. Be sure to spend time developing a strategy rndesigned to leverage the position template throughout each employee’s life rncycle. A couple of key areas where this information can make a direct impact are rnselection and succession planning. Also, make a point to study your progress (after a sufficient period of time has elapsed) and make adjustments based on rnyour study findings.

Learning Objectives

• Learn to select employees from the candidate pool who best represent the rncollection of ideal behaviors important to success in the position…and make the rnbest selection on a consistent basis.
• Learn to develop individual succession planning strategies for each position rnbased on the position templates that you create.
• Learn to study the performance of employees hired and developed using a rnposition template.

Selection

Selecting the right people for the right positions is always a great place to rnuse your position template created using successful traits. Employee selection rnwill always have a large and immediate impact on your financial bottom line. Any rnsports coach will tell you great players make great coaches, just as any manager rnwill tell you great employees make great managers. Leveraging this information rnin your selection process will improve the odds of finding the best of the best rnfor your position.

Here are a few areas where you can incorporate your position template quickly rnand efficiently in the selection process:

• Pre-screens—This is a very practical way to focus on those candidates who meet rnthe minimum qualifications for the position. Because you now have deeper insight rninto success in the position, you can screen out those who do not have the “right stuff.”
• Phone screens—Here is another way to help your recruiting staff be more rnefficient and performance-minded. Start by sharing your findings. Then provide a rnseries of phone screen questions, based on the desired traits, to help rnrecruiters identify those who have the best chance to succeed in the role.
• Behavioral assessments—synchronize the position template with your assessment rntool to identify “ideal” candidates as part of the application process.
• Face-to-face interviews—Creating a consistent interview process is a tough rntask. Hiring managers often have different opinions as to what contributes to rnsuccess. Now you have an ace in the hole. You know the important traits (recorded in the position template) that drive high performance. A great way to rnincorporate your position template in the selection process is to create a set rnof trait-based interview questions. Be sure to train your hiring managers on the rndata-driven source of these questions and how they are linked to success in the rnposition.

Succession

Succession planning is a long-term jou ey for leveraging your position rntemplates. Most succession planning programs are designed to develop the bench rnstrength at the management level. Any succession planning program requires a rntarget or, in this case, a template to teach, train, and evaluate potential rnfuture performers based on the traits needed to be successful in the role.

Once you have successfully created a position template, you have a map for rnsuccess that can guide your inte al promotion and development programs. From a rnlong-term perspective, think of the possibilities. You have valuable information rnto shape programs at the position level, based on traits linked to performance rndata, which directly reflects your business drivers. If leveraged properly, you rnwill be able to use this information to identify gaps in your training as well rnas create content for individual training plans tailored to each employee and rntheir current and future roles. Do not forget about your ability to coach and rndevelop your workforce more effectively by communicating clear and specific rnexpectations for performance.

Study

A study should occur after an adequate length of time has passed since the rnrollout and implementation of your position template. Since it occurs after the rnrollout, it is generally referred to as a post-deployment study. You should rnschedule time in the future to measure your workforce improvement as it relates rnto the deployment of your position template. It is also sensible to initiate a rnrecalibration of your findings at some point in time based on the findings of rnyour post-deployment study. In other words, your business may change and new rnproducts, expanded markets, and reorganizations all contribute to changes in the rntraits or position template of a job. Always keep in mind that anytime you rnchange the way you measure job performance, you increase the likelihood of rnchanging the traits and the position template.

The goal of a post-deployment study is to ensure that the position template rncontinues to represent the desired traits most conducive to success in the rnposition. Prior to conducting a post-deployment study, there are a few tips to rnremember:

• Be patient and give your process time to “bake.” Many companies make the rnmistake of attempting to study progress without considering the time needed for rnfull implementation or new hire ramp-up. It typically takes 12 to 24 months to rnsee the full effect of the changes you have made in the workforce. I was rninvolved in one situation where a business executive wanted to know why his rncompany’s turnover rate had not decreased during the first few weeks of a new rnhiring strategy deployment. We kindly explained to him that he needed to hire rnpeople based on the newly defined behavioral traits for a period of rntime—allowing them to gain some experience on the job—before there would be rnenough data to evaluate performance and tenure.
• Set realistic expectations—You will have the ability to examine every position rnand extract key traits, but not all positions will qualify for a follow-up rnstudy. In some positions, there may not be enough employees or turnover to rneffectively evaluate the direct impact at an aggregate level.
• Clean the data—When following up, be sure that you focus your efforts on rnrelevant samples of people. Do not mix different positions in the same study rngroup. Narrow the sample to employees that have been on the job long enough to rnpossess an adequate work record, and remove any data that will contaminate the rnsample and produce inaccurate conclusions.

One Company’s Results after Following the Productivity Cycle

HSBC, one of the largest financial organizations in the world, used the rnprinciples described in the Productivity Cycle to increase sales generated by rnemployees in the position of Account Executive. HSBC Account Executives initiate rnloan sales and provide customer service, two activities that directly influence rncompany profits. After methodically cataloging, transforming, and systemizing rnthe behaviors of those in the position, HSBC selected new employees that sold 21% more loans than coworkers who were hired outside of the Productivity Cycle rnprocess. This figure was based on the post-deployment study of (n = 2,040) employees in the role. (To review the full case study, visit www.PeopleAnswers.com.)

Summary

HSBC is just one example of a large company that saw an opportunity to rntransform an important sales position into a stronger profit center. The 9-step Productivity Cycle succeeded in raising the bar for average sales by 21%. Could rnyour organization make room for a 21% sales increase? How about 30% more calls rnhandled by telephone representatives? A 40% reduction in annual turnover? The rnsky is the limit once you have worked your way through the Productivity Cycle rnfor a specific position. The results may warrant applying the process to all of rnyour job positions over time.